We compare our MLP ranker to a bidirectional LSTM [1]} model. It takes two sequences inputs: sequence 1 and sequence 2, and a stack of three bidirectional LSTM layers, which encode sequence 1 and sequence 2, respectively. The outputs are then concatenated, to enable representing the differences between the two sequences. Instead of relying only on this presentation [2]}, [3]}, we also concatenate our distance features and feed everything into our MLP ranker described above.
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